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Molecules 2018, 23(5), 1016; https://doi.org/10.3390/molecules23051016

Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages

School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China
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Academic Editor: Quan Zou
Received: 4 April 2018 / Revised: 23 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Discovering the common modules that are co-expressed across various stages can lead to an improved understanding of the underlying molecular mechanisms of cancers. There is a shortage of efficient tools for integrative analysis of gene expression and protein interaction networks for discovering common modules associated with cancer progression. To address this issue, we propose a novel regularized multi-view subspace clustering (rMV-spc) algorithm to obtain a representation matrix for each stage and a joint representation matrix that balances the agreement across various stages. To avoid the heterogeneity of data, the protein interaction network is incorporated into the objective of rMV-spc via regularization. Based on the interior point algorithm, we solve the optimization problem to obtain the common modules. By using artificial networks, we demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy. Furthermore, the rMV-spc discovers common modules in breast cancer networks based on the breast data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm effectively integrate heterogeneous data for dynamic modules. View Full-Text
Keywords: conserved modules; network analysis; subspace clustering; regularization; protein interaction networks conserved modules; network analysis; subspace clustering; regularization; protein interaction networks
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Zhang, E.; Ma, X. Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages. Molecules 2018, 23, 1016.

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